1. Field of Invention
The current invention relates to processing image data, and more particularly to model-based processing of image data.
2. Discussion of Related Art
The contents of all references, including articles, published patent applications and patents referred to anywhere in this specification are hereby incorporated by reference.
In tomographic imaging, there are many situations in which portions of the image volume are known a priori. Examples in orthopedics include a component for which exact knowledge may be known, for example, pedicle screws and rods for spine surgery, knee and hip implants for joint replacements, and plates and screws for fixation in trauma cases. In image-guided procedures, surgical tools are often placed within the imaging field. When these components are metallic, measurements whose projections contain those elements can suffer from reduced signal-to-noise ratio due to photon starvation. Similarly, since reconstruction of highly attenuating components involves mathematical inversion of near zero projection values, algorithms tend to be very sensitive to any biases (e.g., due to polyenergetic effects). Both of these effects tend to produce streak artifacts in the reconstructed images [B. De Man, et al., “Metal streak artifacts in X-ray computed tomography: A simulation study,” IEEE Trans Nuclear Science, vol. 46, pp. 691-696, 1999; J. F. Barrett and N. Keat, “Artifacts in CT: recognition and avoidance,” Radiographics, vol. 24, pp. 1679-91, November-December 2004].
Such artifacts tend to be particularly troublesome since it is often the region immediately surrounding the component that is of diagnostic interest, which is exactly where the artifacts tend to be most pronounced. Particular situations where image quality in the neighborhood of a metallic component is critical include the visualization around implants for indications of subsidence or osteolysis [S. D. Stulberg, et al., “Monitoring pelvic osteolysis following total hip replacement surgery: an algorithm for surveillance,” J Bone Joint Surg Am, vol. 84-A Suppl 2, pp. 116-22, 2002], assessment of pedicle screw placement to avoid critical structures in the spine [L. T. Holly and K. T. Foley, “Three-dimensional fluoroscopy-guided percutaneous thoracolumbar pedicle screw placement. Technical note,” J Neurosurg, vol. 99, pp. 324-9, October 2003; M. Y. Wang, et al., “Reliability of three-dimensional fluoroscopy for detecting pedicle screw violations in the thoracic and lumbar spine,” Neurosurgery, vol. 54, pp. 1138-42; discussion 1142-3, May 2004], and biopsy needle guidance [B. Daly, et al., “Percutaneous abdominal and pelvic interventional procedures using CT fluoroscopy guidance,” AJR Am J Roentgenol, vol. 173, pp. 637-44, September 1999].
Various approaches have been developed to mitigate metal streak artifacts [B. De Man, et al., “Reduction of metal steak artifacts in X-ray computed tomography using a transmission maximum a posteriori algorithm,” IEEE Trans Nuclear Science, vol. 47, pp. 977-981, 2000 2000; G. H. Glover and N. J. Pelc, “An algorithm for the reduction of metal clip artifacts in CT reconstructions,” Med Phys, vol. 8, pp. 799-807, November-December 1981; W. A. Kalender, et al., “Reduction of CT artifacts caused by metallic implants,” Radiology, vol. 164, pp. 576-7, August 1987; H. Li, et al., “Metal artifact suppression from reformatted projections in multislice helical CT using dual-front active contours,” Med Phys, vol. 37, pp. 5155-64, October 2010; D. D. Robertson, et al., “Total hip prosthesis metal-artifact suppression using iterative deblurring reconstruction,” J Comput Assist Tomogr, vol. 21, pp. 293-8, March-April 1997; G. Wang, et al., “Iterative deblurring for CT metal artifact reduction,” IEEE Trans Med Imaging, vol. 15, pp. 657-64, 1996; O. Watzke and W. A. Kalender, “A pragmatic approach to metal artifact reduction in CT: merging of metal artifact reduced images,” Eur Radiol, vol. 14, pp. 849-56, May 2004; B. P. Medoff, et al., “Iterative Convolution Backprojection Algorithms for Image-Reconstruction from Limited Data,” Journal of the Optical Society of America, vol. 73, pp. 1493-1500, 1983; J. Rinkel, et al., “Computed tomographic metal artifact reduction for the detection and quantitation of small features near large metallic implants: a comparison of published methods,” J Comput Assist Tomogr, vol. 32, pp. 621-9, July-August 2008].
Many methods consider measurements through metal to be missing data. The missing data can simply be eliminated from the reconstruction algorithm [B. P. Medoff, et al., “Iterative Convolution Backprojection Algorithms for Image-Reconstruction from Limited Data,” Journal of the Optical Society of America, vol. 73, pp. 1493-1500, 1983], or may be filled in using values based on the neighborhood of the missing data [G. H. Glover and N. J. Pelc, “An algorithm for the reduction of metal clip artifacts in CT reconstructions,” Med Phys, vol. 8, pp. 799-807, November-December 1981; W. A. Kalender, et al., “Reduction of CT artifacts caused by metallic implants,” Radiology, vol. 164, pp. 576-7, August 1987]. However, rarely is the exact knowledge of the metal component used.
Tomographic imaging generally benefits from the incorporation of prior knowledge into the reconstruction algorithm. This is particularly true for situations that involve under-sampling and low signal-to-noise. Methods that seek to correct for metal streak artifacts tend to require identification of spatial locations in the volume, or the locations in the projection image where the metal implant lies. This localization typically relies on knowledge that the metal components have a high attenuation coefficient. In effect, this is a relatively weak incorporation of prior knowledge.
In penalized-likelihood reconstruction schemes, general knowledge about the image can be included via Gibbs priors or penalty functions [K. Lange, “Convergence of EM image reconstruction algorithms with Gibbs smoothing,” IEEE Trans Med Imaging, vol. 9, pp. 439-46, 1990; T. Hebert and R. Leahy, “A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors,” IEEE Trans Med Imaging, vol. 8, pp. 194-202, 1989; J. B. Thibault, et al., “A three-dimensional statistical approach to improved image quality for multislice helical CT,” Med Phys, vol. 34, pp. 4526-44, November 2007; J. Wang, et al., “Iterative image reconstruction for CBCT using edge-preserving prior,” Med Phys, vol. 36, pp. 252-60, January 2009].
In more recent work, very specific image priors that incorporate prior scans of the anatomy have been used in algorithms like PICCS [G. H. Chen, et al., “Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,” Med Phys, vol. 35, pp. 660-3, February 2008] and modified penalized-likelihood approaches [J. Stayman, et al., “Penalized-likelihood reconstruction for sparse data acquisitions with unregistered prior images and compressed sensing penalties,” in SPIE Medical Imaging, 2011]. However, these approaches still result in low imaging quality.
There is thus a need for improved processing of image data.